Li, Housen

Authors Requardt R, Li H Journal Arxiv Citation arXiv:2607.03088 Abstract In many modern experiments, latent interactions drive multicomponent stochastic systems, yet the data are available only as pooled measurements that obscure these dependencies. Whether such interactions can be identified and inferred from aggregate signals remains largely unexplored. Motivated by multi-channel

Authors Mösching A, Li H, Munk A Journal Journal of Computational and Graphical Statistics Citation Journal of Computational and Graphical Statistics, 1–15. Abstract Hidden Markov models (HMMs) are characterized by an unobservable Markov chain and an observable process—a noisy version of the hidden chain. Decoding the original signal from the

Authors Li H, Liu Z, Munk A Journal IEEE Transactions on Information Theory Citation EEE Transactions on Information Theory, 72, 2, 1240-1275, Feb. 2026. Abstract Modern large-scale data analysis increasingly faces the challenge of achieving computational efficiency as well as statistical accuracy, as classical statistically efficient methods often fall short

Authors Hüselitz A, Li H, Munk A Journal Journal of Time Series Analysis Citation Journal of Time Series Analysis 1–22. Abstract We consider the problem of sequential (online) estimation of a single change point in a piecewise linear regression model under a Gaussian setup. We demonstrate that certain CUSUM-type statistics

Authors Requadt R, Li H, Munk A Journal Arxiv Citation arXiv:2510.05645. Abstract We investigate the asymptotic behavior of parametric Bayes estimators under a broad class of loss functions that extend beyond the classical translation-invariant setting. To this end, we develop a unified theoretical framework for loss functions exhibiting locally polynomial

Authors Lui C, Trillos NG, Li H, Suchan L Journal Arxiv Citation arXiv:2507.18803. Abstract Given i.i.d.\ samples Xn={x1,…,xn} from a distribution supported on a low dimensional manifold M embedded in Eucliden space, we consider the graph Laplacian operator Δn associated to an ε-proximity graph over Xn and study the asymptotic

Authors Cho H, Kley T, Li H Journal Journal of the Royal Statistical Society, Series B Citation Journal of the Royal Statistical Society Series B: Statistical Methodology, Volume 87, Issue 5, November 2025, Pages 1528–1552. Abstract For data segmentation in high-dimensional linear regression settings, the regression parameters are often assumed

Authors Hüselitz A, Li H, Munk A Journal Arxiv Citation arXiv:2503.05270. Abstract We consider the problem of sequential estimation of a single change point in a piecewise linear regression model under a Gaussian setup. We demonstrate that a certain CUSUM-type statistic attains the minimax optimal rates for localizing the change

Authors Requardt R, Fink M, Kubica P, Steinem C, Munk A, Li H   Journal IEEE Transactions on NanoBioScience (TNB)   Citation IEEE Transactions on NanoBioScience (TNB). 2025.   Abstract Recent experimental studies have shed light on the intriguing possibility that ion channels exhibit cooperative behaviour. However, a comprehensive understanding

Authors Kovacs S, Li H, Haubner L, Munk A, Buhlmann P Journal Journal of Machine Learning Research Citation J Mach Learn Res 25(297):1−64, 2024. Abstract Change point estimation is often formulated as a search for the maximum of a gain function describing improved fits when segmenting the data. Searching one